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2006

Gene function classification using Bayesian models with hierarchy-based priors

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Gene function classification using Bayesian models with hierarchy-based priors
We investigate the application of hierarchical classification schemes to the annotation of gene function based on several characteristics of protein sequences including phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and a MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome. The results from all three models show substantial improvement over previous methods, which were based on the C5 algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL mo...
Babak Shahbaba, Radford M. Neal
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2006
Where BMCBI
Authors Babak Shahbaba, Radford M. Neal
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